• QR Code (size=2)
• QR Code (size=3)
• QR Code (size=4)
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• QR Code (size=6)

5.6
4.4
2.4
4.8
3.2
4
4
4.4
6.4
4.4
5.2
4.8
3.2
4.8
4.4
1.6
3.6
3.2
3.2
5.6
6
6.4
3.6
5.6
4.4
3.2
3.6
3.6
3.6
3.6
4
6.4
4.4
3.2
3.6
6.4
4.4
6.4
4.8
4.8
5.6
3.6
4
3.6
4
4.8
5.6
5.6
4
5.6
6.4
3.6
4
2.4
3.2
5.2
4
3.2
2.8
6
3.6
4
4.8
5.2
4
4.4
3.2
3.6
5.2
4.4
3.2
3.6
3.6
6
3.6
4
5.6
4.8
4.8
4.4
5.6
2.4
4.8
3.2
5.6
4.4
4
5.6
4.8
4
5.6
2
4.4
4
3.6
4
6.4
5.2
3.6
4
4
2.8
3.6
3.2
5.6
5.6
3.2
3.6
5.6
5.6
3.2
3.2
3.2
2.8
2.4
3.2
2.4
4.4
5.6
4.4
4.4
4.4
5.6
3.2
8
4.4
3.2
4.4
4
5.6
4.4
3.6
3.6
3.2
4
5.2
5.2
4.8
3.2
5.2
5.6
4.8
5.6
6
5.2
6.4
3.6
3.6
3.6
3.2
2.8
6.4
4.4
3.6
4.4
3.6
5.6
5.2
6.4

5.5
3.5
8.5
5
6
6
5.5
5.5
6
6.5
7
8
5.5
5
5.5
7.5
4.5
5.5
8.5
8.5
5.5
9
7
5
5.5
7.5
7.5
6.5
8
6.5
4.5
9
9
6
8.5
4.5
4.5
6
9
6
9
7
7.5
8
5
5.5
7
4.5
6
8.5
2.5
6
6
3
12
6
6
7
3.5
6.5
6
6.5
7
4
5.5
4.5
5.5
6.5
5
5.5
6
4.5
7.5
9
7.5
6
6.5
7
5
6.5
6.5
5.5
6.5
8
4
8
5.5
4.5
8
6
7
4
4.5
7.5
5.5
10.5
7
9
6
6.5
7.5
6
9.5
7.5
5.5
5.5
5
6.5
7.5
6
6
8
4.5
9
4
6.5
8.5
4.5
7.5
4
3.5
6
7
3
4
8.5
5
5.5
7
5.5
6.5
6
5.5
4.5
6
10
6
6.5
6
6
4.5
7.5
12
3.5
8.5
5.5
8.5
5.5
6
7
5.5
8
10.5
7
10
6.5
5.5
7.5
9.5

par1 <- as.numeric(par1)
library(lattice)
z <- as.data.frame(cbind(x,y))
m <- lm(y~x)
summary(m)
bitmap(file='test1.png')
plot(z,main='Scatterplot, lowess, and regression line')
lines(lowess(z),col='red')
abline(m)
grid()
dev.off()
bitmap(file='test2.png')
m2 <- lm(m$fitted.values ~ x)
summary(m2)
z2 <- as.data.frame(cbind(x,m$fitted.values))
names(z2) <- list('x','Fitted')
plot(z2,main='Scatterplot, lowess, and regression line')
lines(lowess(z2),col='red')
abline(m2)
grid()
dev.off()
bitmap(file='test3.png')
m3 <- lm(m$residuals ~ x)
summary(m3)
z3 <- as.data.frame(cbind(x,m$residuals))
names(z3) <- list('x','Residuals')
plot(z3,main='Scatterplot, lowess, and regression line')
lines(lowess(z3),col='red')
abline(m3)
grid()
dev.off()
bitmap(file='test4.png')
m4 <- lm(m$fitted.values ~ m$residuals)
summary(m4)
z4 <- as.data.frame(cbind(m$residuals,m$fitted.values))
names(z4) <- list('Residuals','Fitted')
plot(z4,main='Scatterplot, lowess, and regression line')
lines(lowess(z4),col='red')
abline(m4)
grid()
dev.off()
bitmap(file='test5.png')
myr <- as.ts(m$residuals)
z5 <- as.data.frame(cbind(lag(myr,1),myr))
names(z5) <- list('Lagged Residuals','Residuals')
plot(z5,main='Lag plot')
m5 <- lm(z5)
summary(m5)
abline(m5)
grid()
dev.off()
bitmap(file='test6.png')
hist(m$residuals,main='Residual Histogram',xlab='Residuals')
dev.off()
bitmap(file='test7.png')
if (par1 > 0)
{
densityplot(~m$residuals,col='black',main=paste('Density Plot   bw = ',par1),bw=par1)
} else {
densityplot(~m$residuals,col='black',main='Density Plot')
}
dev.off()
bitmap(file='test8.png')
acf(m$residuals,main='Residual Autocorrelation Function')
dev.off()
bitmap(file='test9.png')
qqnorm(x)
qqline(x)
grid()
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Simple Linear Regression',5,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Statistics',1,TRUE)
a<-table.element(a,'Estimate',1,TRUE)
a<-table.element(a,'S.D.',1,TRUE)
a<-table.element(a,'T-STAT (H0: coeff=0)',1,TRUE)
a<-table.element(a,'P-value (two-sided)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'constant term',header=TRUE)
a<-table.element(a,m$coefficients[[1]])
sd <- sqrt(vcov(m)[1,1])
a<-table.element(a,sd)
tstat <- m$coefficients[[1]]/sd
a<-table.element(a,tstat)
pval <- 2*(1-pt(abs(tstat),length(x)-2))
a<-table.element(a,pval)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'slope',header=TRUE)
a<-table.element(a,m$coefficients[[2]])
sd <- sqrt(vcov(m)[2,2])
a<-table.element(a,sd)
tstat <- m$coefficients[[2]]/sd
a<-table.element(a,tstat)
pval <- 2*(1-pt(abs(tstat),length(x)-2))
a<-table.element(a,pval)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')